The results section is where you finally report your findings. While doing so, you are presenting what you have discovered using different presentation structures, including text, figures, and statistical analyses. You remember the methods section, where you were presenting how you found what you found? In the results, you basically mention what you found.

While you may be heavily inclined to give an opinion on your findings, it is important you don't present any opinion or subjective interpretation; your explanations and meanings can later be presented and elaborated on in the Discussion section, representing your results as raw data.

Thus, try to avoid subjective language. For instance, instead of "The data surprisingly showed a dramatic increase," you should write something very objective like "The temperature increased from 20°C to 35°C over 30 minutes." Results are usually written in the past tense, and you need to focus on being concise, avoiding any redundancy or verbosity as you are trying to present much information.

As mentioned, discussion and results are closely related, given that the data you presented in the results will be your baseline for what you are writing in the discussion section. You thus usually need to write the results section first before the discussion, so it would be more convenient for you, given that all data points would already be there.

You remember reporting bias, where researchers may selectively report findings that are better or more significant? In such cases, while extreme, a researcher may even omit negative or nonsignificant results. Thus, you need to not omit any results just because they didn't support your hypothesis; don't ignore any of your results, even if they were negative. All pieces of results are equally valuable and informative; thus, you should mention them all.

While you need to be as comprehensive and inclusive as possible, always focus on being simple and non-redundant. This section should mainly be a summary, yet it is not necessary to include every single data piece you gathered, if this data is irrelevant to your research question. That is, the section should be a brief overview of the findings yet not an overly complete presentation of every single data point.

When you will be doing your research later on and use many scholarly papers, you would see that these authors usually have many heavily informative tables and figures. When doing so, you need to always discuss the data in those illustrations or charts. That is, if you didn't reference a table or graph in your section, do not include it.

In research, there is a huge difference between what the very basic public reader and another author understand about defining terms and concepts. For your results section, you should assume that readers understand, at least at a basic level, how statistical data was collected. For instance, you don't need to define what Chi-squared and t-tests are, only reporting your results and findings. Remember that it is not your job to teach your readership how to analyze statistics, yet to only present your results. (More on those definitions later.)

The following is an example of California State University Bakersfield.

Three of the concentration treatments had germinated seeds. The wheat seeds treated with 3.5 % concentration of saline did not develop after germination. The distilled water (0% salinity) treatments had the greatest percentage of germination of all four treatments (Table 1). There was a negative linear relationship between the percentage of germination and salinity treatment (Figure 1). The wheat seeds given distilled water had the highest mean growth for the four treatments. The mean growth for seedlings given 1% and 2% salinity did not differ significantly (Figure 2). There were no seedlings observed for the 3.5 % treatment. The three treatments with salt did not have any fungal growth, but the distilled watered seedlings did have fungal growth in the petridishes.

Here is another example from “Medical writing curriculum: how to write the methodology and results sections” by Jeremy Dean Chapnick:

“Age, sex, body surface area (BSA) and peak contrast enhancement (PCE) were significant predictors for VCE (P<0.05). A strong linear correlation was observed between VCE and contrast volume ( r = 0.97 P < 0.05 ) . The 100-to-120 kVp contrast enhancement conversion factor (E) was calculated at 0.81. Optimal VCE (250 to 450 HU) and diagnostic image quality were obtained with significant reductions in TID (32.1%) and radiation dose (38.5%) when using 100 kVp and personalized contrast volume calculation algorithm compared with 120 kVp and routine contrast protocols (P < 0.05).”

As you can see in this example, the findings were simply presented without any interpretation, being fully unbiased and not having any commentary or subjective opinions. The focus is entirely on reporting raw data collected. As you can also see, the paragraph is written with high clarity and conciseness.